Machine learning for automated, targeted, phototherapy

Abstract

This work combines two existing technologies to demonstrate the possibility for automated, targeted, phototherapy of psoriasis and other skin conditions: 1) Image-to-image translation via a neural network (NN) as a method of image segmentation. 2) Light control using a digital micromirror device (DMD). With a small dataset of just 104 patient photographs (labeled by expert dermatologists) our NN model was trained to identify regions of psoriasis that required treatment, achieving an average accuracy of 96.6%. The image output of the NN model was applied to a DMD and precise control over the shape of the illuminated region was demonstrated. In the proposed automated phototherapy device this would target treatment to the affected regions, minimizing exposure of healthy skin and the associated risks of patient harm.